{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 数据准备与预处理"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5b9bcc1863084769"
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-30T12:54:13.442244Z",
     "start_time": "2025-03-30T12:54:09.733414Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import precision_score,recall_score, confusion_matrix,f1_score,accuracy_score, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# 加载 MNIST 数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 调用scikit-learn库实现感知机模型的训练与评估"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "af013e3e053a13c0"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 将数据分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 数据归一化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "# 创建感知机模型\n",
    "model = Perceptron(max_iter=1000, random_state=42)\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "# 使用模型进行预测\n",
    "y_pred = model.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-03-30T12:54:24.207633Z",
     "start_time": "2025-03-30T12:54:13.658664Z"
    }
   },
   "id": "283d1b46570c3494",
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率: 0.8645714285714285\n",
      "精确率: 0.875970639476072\n",
      "召回率: 0.8645714285714285\n",
      "F1-分数: 0.8646109180743257\n",
      "混淆矩阵:\n",
      " [[1322    1    3    0    2    1    3    1    6    4]\n",
      " [   1 1523    7    7    3    8    2    1   44    4]\n",
      " [  29   24 1116   26   21    8   32    7  109    8]\n",
      " [  40   10   33 1210    6   19   13    4   62   36]\n",
      " [   5    1    8    3 1121    2    3    6   17  129]\n",
      " [  80    9    8   66   28  835   15    6  182   44]\n",
      " [  19    2   15    0   18   15 1309    0   15    3]\n",
      " [  24    1   20   10   25    1    0 1197   17  208]\n",
      " [  44   18   11   22   10   11    8    7 1174   52]\n",
      " [  18    5    2    8   43    1    0   23   23 1297]]\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.98      0.90      1343\n",
      "           1       0.96      0.95      0.95      1600\n",
      "           2       0.91      0.81      0.86      1380\n",
      "           3       0.89      0.84      0.87      1433\n",
      "           4       0.88      0.87      0.87      1295\n",
      "           5       0.93      0.66      0.77      1273\n",
      "           6       0.95      0.94      0.94      1396\n",
      "           7       0.96      0.80      0.87      1503\n",
      "           8       0.71      0.87      0.78      1357\n",
      "           9       0.73      0.91      0.81      1420\n",
      "\n",
      "    accuracy                           0.86     14000\n",
      "   macro avg       0.87      0.86      0.86     14000\n",
      "weighted avg       0.88      0.86      0.86     14000\n"
     ]
    }
   ],
   "source": [
    "# 评估模型\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='weighted')\n",
    "recall = recall_score(y_test, y_pred, average='weighted')\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(\"准确率:\", accuracy)\n",
    "print(\"精确率:\", precision)\n",
    "print(\"召回率:\", recall)\n",
    "print(\"F1-分数:\", f1)\n",
    "print(\"混淆矩阵:\\n\", conf_matrix)\n",
    "print(\"分类报告:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-03-30T12:56:56.086284Z",
     "start_time": "2025-03-30T12:56:56.019297Z"
    }
   },
   "id": "bbfbb922f9e08a95",
   "execution_count": 52
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 使用热度图展示混淆矩阵"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cf7f920b5dd42c1e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Purples')\n",
    "plt.title(\"Perceptron - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-03-30T13:00:36.935286Z",
     "start_time": "2025-03-30T13:00:36.034457Z"
    }
   },
   "id": "7057341607f8b69f",
   "execution_count": 65
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "919e7c48eca38947"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
